Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Early origin of the bilaterian developmental toolkit

Abstract: Whole-genome sequences from the choanoflagellate Monosiga brevicollis, the placozoan Trichoplax adhaerens and the cnidarian Nematostella vectensis have confirmed results from comparative evolutionary developmental studies that much of the developmental toolkit once thought to be characteristic of bilaterians appeared much earlier in the evolution of animals. The diversity of transcription factors and signalling pathway genes in animals with a limited number of cell types and a restricted developmental repertoire is puzzling, particularly in light of claims that such highly conserved elements among bilaterians provide evidence of a morphologically complex protostome–deuterostome ancestor. Here, I explore the early origination of elements of what became the bilaterian toolkit, and suggest that placozoans and cnidarians represent a depauperate residue of a once more diverse assemblage of early animals, some of which may be represented in the Ediacaran fauna (c. 585–542 Myr ago).

The Pace of Cultural Evolution

Abstract: Today, humans inhabit most of the world’s terrestrial habitats. This observation has been explained by the fact that we possess a secondary inheritance mechanism, culture, in addition to a genetic system. Because it is assumed that cultural evolution occurs faster than biological evolution, humans can adapt to new ecosystems more rapidly than other animals. This assumption, however, has never been tested empirically. Here, I compare rates of change in human technologies to rates of change in animal morphologies. I find that rates of cultural evolution are inversely correlated with the time interval over which they are measured, which is similar to what is known for biological rates. This correlation explains why the pace of cultural evolution appears faster when measured over recent time periods, where time intervals are often shorter. Controlling for the correlation between rates and time intervals, I show that (1) cultural evolution is faster than biological evolution; (2) this effect holds true even when the generation time of species is controlled for; and (3) culture allows us to evolve over short time scales, which are normally accessible only to short-lived species, while at the same time allowing for us to enjoy the benefits of having a long life history.

Homophily and minority-group size explain perception biases in social networks

Abstract: People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society. Lee et al. show people's biases in social perception can be explained merely by the structure of their social networks, without assuming biased cognition. Social perception biases can be explained by homophily of personal networks and minority-group size.

Phase transitions in random networks : simple analytic determination of critical points

Abstract: The standard method for determining the critical points in random networks (such as Boolean nets) is based on the Derrida approximation. This method leads to the critical points based on the so-called annealed approximation. In this paper, we present a very simple annealed approximation based on the study of damage spreading when single elements are modified. Several examples are analyzed.